{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.model_selection as skm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X_train,X_test,y_train,y_test =skm.train_test_split(boston.data,\n",
    "                                                    boston.target,\n",
    "                                                    test_size=0.25,\n",
    "                                                    random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33.8, 20.3, 10.2, 22. , 21.2, 24.2, 29. , 22.7, 21.8, 34.9, 25.2,\n",
       "       20.9, 19.4, 20. , 14. , 30.1, 33.1, 20.6, 22.6, 33.4, 20.1, 10.5,\n",
       "       15.6, 16.8, 22.6, 34.6, 19.8, 17.8, 22. , 17.4, 15.4, 16.7, 22.6,\n",
       "       15.1, 21.4, 15.3,  7.4, 13.9, 17.6, 25. , 46.7, 17.1, 23.1, 18.7,\n",
       "       21.9, 18.9, 26.7, 22.3, 25. , 14.6, 42.8, 17.3, 22.2, 36.5, 22.8,\n",
       "       19.9, 36.2, 50. , 25. , 22.2, 17.5, 23.9, 19.6, 24.7, 28.4,  8.7,\n",
       "       21.7, 20. , 19.9, 24.5, 15. ,  7. , 15.2, 20.4,  8.5, 17.1, 30.1,\n",
       "       15. , 19.4, 23.2, 17. , 18.9, 50. , 25. , 46. ,  7.2, 17.8, 35.1,\n",
       "       24.3,  5. , 16.6, 21.8, 28.5, 22. , 20.3, 21.7, 26.4, 30.7, 50. ,\n",
       "       17.2, 26.6, 21. , 23.4, 19.5, 20.7, 23.3, 48.8, 15.6, 19.6, 17.4,\n",
       "       21.7, 14.6, 37.9,  9.7, 17.8, 12.1, 20.1, 29.9, 26.4, 18.8, 32.5,\n",
       "       15.7, 13.4, 21.7, 23.6, 11.9, 13.8, 22.2, 13. , 33.2, 50. , 22.3,\n",
       "       22.4, 23.8, 29.1, 20.8, 23.7, 19.8, 13.9, 28.4, 45.4, 23.7, 50. ,\n",
       "       18. , 17.1, 18.9, 10.4, 24.7, 23.9, 23. , 20.2,  8.5, 14.2, 20.3,\n",
       "       18.5, 12. , 19.3, 20.6, 16.1, 12.3, 23.1, 22.7, 20.3, 16.7, 27.9,\n",
       "       21.4,  8.1, 37.6, 15.6, 29.6, 22.9, 24.8, 24.4, 50. , 28.7, 50. ,\n",
       "       16.5, 18.2, 50. , 16.2, 14.1, 21.2, 18.4, 25. , 50. , 21.2, 20.4,\n",
       "       15.2, 22. , 19.8, 22.1, 23.9, 24.6, 23.9, 21.7, 44.8,  7.2, 18.5,\n",
       "       20.1, 23.3, 19.2, 29.1, 31. , 22.9, 27.5, 39.8, 22. , 22.8, 22.9,\n",
       "       14.3, 14.5, 22.4, 19.3, 32. , 20.1, 18.3, 24.5, 18.4, 23.1, 22.6,\n",
       "       20.2, 17.8, 31.6, 43.5, 36.4, 11.3, 20.5, 23.2, 29.8, 20.6, 24.3,\n",
       "       18.1, 19.1, 21.4, 31.5, 19.2, 14.3, 24.8, 21.1, 18.2, 48.3, 19.4,\n",
       "       21.2, 10.9, 27.5, 34.7, 14.4, 22.8, 17.8, 50. , 24.4, 12.8, 30.8,\n",
       "       28.2, 25. , 33.1, 27.5, 12.7, 43.1, 13.4, 21.5, 33.4, 23.8, 21. ,\n",
       "       26.6, 18.5, 23. , 24.1, 20.5, 32.2, 14.4, 11.8, 19.5, 23.7, 13.2,\n",
       "       29. , 18.2, 18.6, 23. , 42.3, 17.2, 16.2, 20. , 30.3, 20.9, 20.4,\n",
       "       24.8, 18.7, 16.8, 22.5, 18.8, 23.7, 23.8, 19.6, 20.4, 16.1, 44. ,\n",
       "       19.3, 17.4, 10.2, 11.7, 37.2, 11. , 23.6, 22.8, 15. , 34.9, 17.9,\n",
       "       24.4, 24.5,  6.3, 29.4, 10.4, 38.7, 20. , 19.4, 37. , 50. , 18.7,\n",
       "       48.5, 35.4, 23.4,  7. , 50. , 20.7, 35.4,  9.6, 25.1, 16.1, 27. ,\n",
       "       16.6, 13.3, 25. , 24. , 19.6, 29.6, 21.7, 19.1, 22. , 13.3, 27.1,\n",
       "       22.9, 33.2, 13.5, 14.5,  8.3, 41.7, 31.2, 23.9, 23.1, 24.3, 18.3,\n",
       "       20.8, 28. , 19.5, 21.5, 13.1, 12.5, 31.7, 13.1, 23.1, 14.5, 22.2,\n",
       "       13.1, 37.3, 22. , 10.2,  5. , 19.3, 16. , 18.6, 50. , 31.6, 24.1,\n",
       "       15.6, 19.4, 23.3, 23.2, 13.6])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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